from typing import Dict, Any, TypedDict
from langgraph.graph import StateGraph, END
from langchain_core.runnables import RunnableConfig
from textblob import TextBlob
import re
import time

# 定義狀態(tài)
class AgentState(TypedDict):
    text: str
    summary: str
    translation: str
    sentiment: str
    summary_time: float
    translation_time: float
    sentiment_time: float

# 總結(jié)智能體
def summarize_agent(state: AgentState) -> Dict[str, Any]:
    print("總結(jié)智能體：正在運(yùn)行")
    start_time = time.time()
    try:
        text = state["text"]
        ifnot text.strip():
            return {
                "summary": "未提供文本進(jìn)行總結(jié)。",
                "summary_time": 0.0
            }
        time.sleep(2)
        sentences = re.split(r'(?<=[.!?]) +', text.strip())
        scored_sentences = [(s, len(s.split())) for s in sentences if s]
        top_sentences = [s for s, _ in sorted(scored_sentences, key=lambda x: x[1], reverse=True)[:2]]
        summary = " ".join(top_sentences) if top_sentences else"文本太短，無法總結(jié)。"
        processing_time = time.time() - start_time
        print(f"總結(jié)智能體：完成，耗時(shí) {processing_time:.2f} 秒")
        return {
            "summary": summary,
            "summary_time": processing_time
        }
    except Exception as e:
        return {
            "summary": f"總結(jié)錯(cuò)誤：{str(e)}",
            "summary_time": 0.0
        }

# 翻譯智能體
def translate_agent(state: AgentState) -> Dict[str, Any]:
    print("翻譯智能體：正在運(yùn)行")
    start_time = time.time()
    try:
        text = state["text"]
        ifnot text.strip():
            return {
                "translation": "未提供文本進(jìn)行翻譯。",
                "translation_time": 0.0
            }
        time.sleep(3)
        translation = (
            "El nuevo parque en la ciudad es una maravillosa adición. "
            "Las familias disfrutan de los espacios abiertos, y a los ni?os les encanta el parque infantil. "
            "Sin embargo, algunas personas piensan que el área de estacionamiento es demasiado peque?a."
        )
        processing_time = time.time() - start_time
        print(f"翻譯智能體：完成，耗時(shí) {processing_time:.2f} 秒")
        return {
            "translation": translation,
            "translation_time": processing_time
        }
    except Exception as e:
        return {
            "translation": f"翻譯錯(cuò)誤：{str(e)}",
            "translation_time": 0.0
        }

# 情感分析智能體
def sentiment_agent(state: AgentState) -> Dict[str, Any]:
    print("情感分析智能體：正在運(yùn)行")
    start_time = time.time()
    try:
        text = state["text"]
        ifnot text.strip():
            return {
                "sentiment": "未提供文本進(jìn)行情感分析。",
                "sentiment_time": 0.0
            }
        time.sleep(1.5)
        blob = TextBlob(text)
        polarity = blob.sentiment.polarity
        subjectivity = blob.sentiment.subjectivity
        sentiment = "Positive"if polarity > 0else"Negative"if polarity < 0else"Neutral"
        result = f"{sentiment} (Polarity: {polarity:.2f}, Subjectivity: {subjectivity:.2f})"
        processing_time = time.time() - start_time
        print(f"情感分析智能體：完成，耗時(shí) {processing_time:.2f} 秒")
        return {
            "sentiment": result,
            "sentiment_time": processing_time
        }
    except Exception as e:
        return {
            "sentiment": f"情感分析錯(cuò)誤：{str(e)}",
            "sentiment_time": 0.0
        }

# 合并節(jié)點(diǎn)
def join_parallel_results(state: AgentState) -> AgentState:
    return state

# 構(gòu)建圖
def build_parallel_graph() -> StateGraph:
    workflow = StateGraph(AgentState)
    parallel_branches = {
        "summarize_node": summarize_agent,
        "translate_node": translate_agent,
        "sentiment_node": sentiment_agent
    }
    for name, agent in parallel_branches.items():
        workflow.add_node(name, agent)
    workflow.add_node("branch", lambda state: state)
    workflow.add_node("join", join_parallel_results)
    workflow.set_entry_point("branch")
    for name in parallel_branches:
        workflow.add_edge("branch", name)
        workflow.add_edge(name, "join")
    workflow.add_edge("join", END)
    return workflow.compile()

# 主函數(shù)
def main():
    text = (
        "The new park in the city is a wonderful addition. Families are enjoying the open spaces, "
        "and children love the playground. However, some people think the parking area is too small."
    )
    initial_state: AgentState = {
        "text": text,
        "summary": "",
        "translation": "",
        "sentiment": "",
        "summary_time": 0.0,
        "translation_time": 0.0,
        "sentiment_time": 0.0
    }
    print("\n構(gòu)建新圖...")
    app = build_parallel_graph()
    print("\n開始并行處理...")
    start_time = time.time()
    config = RunnableConfig(parallel=True)
    result = app.invoke(initial_state, cnotallow=config)
    total_time = time.time() - start_time
    print("\n=== 并行任務(wù)結(jié)果 ===")
    print(f"輸入文本：\n{text}\n")
    print(f"總結(jié)：\n{result['summary']}\n")
    print(f"翻譯（西班牙語）：\n{result['translation']}\n")
    print(f"情感分析：\n{result['sentiment']}\n")
    print("\n=== 處理時(shí)間 ===")
    processing_times = {
        "summary": result["summary_time"],
        "translation": result["translation_time"],
        "sentiment": result["sentiment_time"]
    }
    for agent, time_taken in processing_times.items():
        print(f"{agent.capitalize()}: {time_taken:.2f} 秒")
    print(f"\n總耗時(shí)： {total_time:.2f} 秒")
    print(f"各任務(wù)總耗時(shí)： {sum(processing_times.values()):.2f} 秒")
    print(f"并行節(jié)省時(shí)間： {sum(processing_times.values()) - total_time:.2f} 秒")

if __name__ == "__main__":
    main()

from typing import Dict
from langgraph.graph import StateGraph, MessagesState, END
from langchain_core.runnables import RunnableConfig
from langchain_core.messages import HumanMessage, AIMessage
import json

# 團(tuán)隊(duì)負(fù)責(zé)人
def team_lead_agent(state: MessagesState, config: RunnableConfig) -> Dict:
    print("團(tuán)隊(duì)負(fù)責(zé)人：開始審批")
    messages = state["messages"]
    proposal = json.loads(messages[0].content)
    title = proposal.get("title", "")
    amount = proposal.get("amount", 0.0)
    ifnot title or amount <= 0:
        status = "Rejected"
        comment = "團(tuán)隊(duì)負(fù)責(zé)人：提案因缺少標(biāo)題或金額無效而被拒絕。"
        goto = END
    else:
        status = "Approved by Team Lead"
        comment = "團(tuán)隊(duì)負(fù)責(zé)人：提案完整且已批準(zhǔn)。"
        goto = "dept_manager"
    print(f"團(tuán)隊(duì)負(fù)責(zé)人：審批完成 - {status}")
    messages.append(AIMessage(
        cnotallow=json.dumps({"status": status, "comment": comment}),
        additional_kwargs={"agent": "team_lead", "goto": goto}
    ))
    return {"messages": messages}

# 部門經(jīng)理
def dept_manager_agent(state: MessagesState, config: RunnableConfig) -> Dict:
    print("部門經(jīng)理：開始審批")
    messages = state["messages"]
    team_lead_msg = next((m for m in messages if m.additional_kwargs.get("agent") == "team_lead"), None)
    proposal = json.loads(messages[0].content)
    amount = proposal.get("amount", 0.0)
    if json.loads(team_lead_msg.content)["status"] != "Approved by Team Lead":
        status = "Rejected"
        comment = "部門經(jīng)理：因團(tuán)隊(duì)負(fù)責(zé)人拒絕而跳過。"
        goto = END
    elif amount > 100000:
        status = "Rejected"
        comment = "部門經(jīng)理：預(yù)算超出限制。"
        goto = END
    else:
        status = "Approved by Department Manager"
        comment = "部門經(jīng)理：預(yù)算在限制范圍內(nèi)。"
        goto = "finance_director"
    print(f"部門經(jīng)理：審批完成 - {status}")
    messages.append(AIMessage(
        cnotallow=json.dumps({"status": status, "comment": comment}),
        additional_kwargs={"agent": "dept_manager", "goto": goto}
    ))
    return {"messages": messages}

# 財(cái)務(wù)總監(jiān)
def finance_director_agent(state: MessagesState, config: RunnableConfig) -> Dict:
    print("財(cái)務(wù)總監(jiān)：開始審批")
    messages = state["messages"]
    dept_msg = next((m for m in messages if m.additional_kwargs.get("agent") == "dept_manager"), None)
    proposal = json.loads(messages[0].content)
    amount = proposal.get("amount", 0.0)
    if json.loads(dept_msg.content)["status"] != "Approved by Department Manager":
        status = "Rejected"
        comment = "財(cái)務(wù)總監(jiān)：因部門經(jīng)理拒絕而跳過。"
    elif amount > 50000:
        status = "Rejected"
        comment = "財(cái)務(wù)總監(jiān)：預(yù)算不足。"
    else:
        status = "Approved"
        comment = "財(cái)務(wù)總監(jiān)：批準(zhǔn)且可行。"
    print(f"財(cái)務(wù)總監(jiān)：審批完成 - {status}")
    messages.append(AIMessage(
        cnotallow=json.dumps({"status": status, "comment": comment}),
        additional_kwargs={"agent": "finance_director", "goto": END}
    ))
    return {"messages": messages}

# 路由函數(shù)
def route_step(state: MessagesState) -> str:
    for msg in reversed(state["messages"]):
        goto = msg.additional_kwargs.get("goto")
        if goto:
            print(f"路由：智能體 {msg.additional_kwargs.get('agent')} 設(shè)置跳轉(zhuǎn)到 {goto}")
            return goto
    return END

# 構(gòu)建 LangGraph
builder = StateGraph(MessagesState)
builder.add_node("team_lead", team_lead_agent)
builder.add_node("dept_manager", dept_manager_agent)
builder.add_node("finance_director", finance_director_agent)
builder.set_entry_point("team_lead")
builder.add_conditional_edges("team_lead", route_step, {
    "dept_manager": "dept_manager",
    END: END
})
builder.add_conditional_edges("dept_manager", route_step, {
    "finance_director": "finance_director",
    END: END
})
builder.add_conditional_edges("finance_director", route_step, {
    END: END
})
workflow = builder.compile()

# 主運(yùn)行器
def main():
    initial_state = {
        "messages": [
            HumanMessage(
                cnotallow=json.dumps({
                    "title": "New Equipment Purchase",
                    "amount": 40000.0,
                    "department": "Engineering"
                })
            )
        ]
    }
    result = workflow.invoke(initial_state)
    messages = result["messages"]
    proposal = json.loads(messages[0].content)
    print("\n=== 審批結(jié)果 ===")
    print(f"提案標(biāo)題：{proposal['title']}")
    final_status = "Unknown"
    comments = []
    for msg in messages[1:]:
        if isinstance(msg, AIMessage):
            try:
                data = json.loads(msg.content)
                if"status"in data:
                    final_status = data["status"]
                if"comment"in data:
                    comments.append(data["comment"])
            except Exception:
                continue
    print(f"最終狀態(tài)：{final_status}")
    print("評(píng)論：")
    for comment in comments:
        print(f"  - {comment}")

if __name__ == "__main__":
    main()

from typing import Dict, Any, List
from langgraph.graph import StateGraph, END
from langchain_core.runnables import RunnableConfig
import textwrap

# 定義狀態(tài)
class EvaluationState(Dict[str, Any]):
    code: str = ""
    feedback: str = ""
    passed: bool = False
    iteration: int = 0
    max_iterations: int = 3
    history: List[Dict] = []

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.setdefault("code", "")
        self.setdefault("feedback", "")
        self.setdefault("passed", False)
        self.setdefault("iteration", 0)
        self.setdefault("max_iterations", 3)
        self.setdefault("history", [])

# 代碼編寫智能體
def code_writer_agent(state: EvaluationState, config: RunnableConfig) -> Dict[str, Any]:
    print(f"迭代 {state['iteration'] + 1} - 代碼編寫智能體：生成代碼")
    print(f"迭代 {state['iteration'] + 1} - 代碼編寫智能體：收到反饋：{state['feedback']}")
    iteration = state["iteration"] + 1
    feedback = state["feedback"]
    if iteration == 1:
        code = textwrap.dedent("""
        def factorial(n):
            result = 1
            for i in range(1, n + 1):
                result *= i
            return result
        """)
        writer_feedback = "初始代碼已生成。"
    elif"factorial(0)"in feedback.lower():
        code = textwrap.dedent("""
        def factorial(n):
            if n == 0:
                return 1
            result = 1
            for i in range(1, n + 1):
                result *= i
            return result
        """)
        writer_feedback = "已修復(fù) n=0 的處理。"
    elif"factorial(-1)"in feedback.lower() or"negative"in feedback.lower():
        code = textwrap.dedent("""
        def factorial(n):
            if n < 0:
                raise ValueError("Factorial not defined for negative numbers")
            if n == 0:
                return 1
            result = 1
            for i in range(1, n + 1):
                result *= i
            return result
        """)
        writer_feedback = "已添加對(duì)負(fù)數(shù)輸入的錯(cuò)誤處理。"
    else:
        code = state["code"]
        writer_feedback = "未發(fā)現(xiàn)進(jìn)一步改進(jìn)的內(nèi)容。"
    print(f"迭代 {iteration} - 代碼編寫智能體：代碼生成完成")
    return {
        "code": code,
        "feedback": writer_feedback,
        "iteration": iteration
    }

# 代碼測(cè)試智能體
def code_tester_agent(state: EvaluationState, config: RunnableConfig) -> Dict[str, Any]:
    print(f"迭代 {state['iteration']} - 代碼測(cè)試智能體：測(cè)試代碼")
    code = state["code"]
    try:
        test_cases = [
            (0, 1),
            (1, 1),
            (5, 120),
            (-1, None)
        ]
        namespace = {}
        exec(code, namespace)
        factorial = namespace.get('factorial')
        ifnot callable(factorial):
            return {"passed": False, "feedback": "未找到階乘函數(shù)。"}
        feedback_parts = []
        passed = True
        for input_val, expected in test_cases:
            try:
                result = factorial(input_val)
                if expected isNone:
                    passed = False
                    feedback_parts.append(f"測(cè)試失?。篺actorial({input_val}) 應(yīng)該拋出錯(cuò)誤。")
                elif result != expected:
                    passed = False
                    feedback_parts.append(f"測(cè)試失?。篺actorial({input_val}) 返回 {result}，預(yù)期為 {expected}。")
            except ValueError as ve:
                if expected isnotNone:
                    passed = False
                    feedback_parts.append(f"測(cè)試失?。篺actorial({input_val}) 意外拋出 ValueError：{str(ve)}")
            except Exception as e:
                passed = False
                feedback_parts.append(f"測(cè)試失敗：factorial({input_val}) 拋出錯(cuò)誤：{str(e)}")
        feedback = "所有測(cè)試通過！"if passed else"\n".join(feedback_parts)
        print(f"迭代 {state['iteration']} - 代碼測(cè)試智能體：測(cè)試完成 - {'通過' if passed else '失敗'}")
        history = state["history"]
        history.append({
            "iteration": state["iteration"],
            "code": code,
            "feedback": feedback,
            "passed": passed
        })
        return {
            "passed": passed,
            "feedback": feedback,
            "history": history
        }
    except Exception as e:
        print(f"迭代 {state['iteration']} - 代碼測(cè)試智能體：測(cè)試失敗")
        return {"passed": False, "feedback": f"測(cè)試錯(cuò)誤：{str(e)}"}

# 條件邊：決定是否繼續(xù)循環(huán)
def should_continue(state: EvaluationState) -> str:
    if state["passed"] or state["iteration"] >= state["max_iterations"]:
        print(f"迭代 {state['iteration']} - {'循環(huán)停止：測(cè)試通過' if state['passed'] else '循環(huán)停止：達(dá)到最大迭代次數(shù)'}")
        return"end"
    print(f"迭代 {state['iteration']} - 循環(huán)繼續(xù)：測(cè)試失敗")
    return"code_writer"

# 構(gòu)建 LangGraph 工作流
workflow = StateGraph(EvaluationState)
workflow.add_node("code_writer", code_writer_agent)
workflow.add_node("code_tester", code_tester_agent)
workflow.set_entry_point("code_writer")
workflow.add_edge("code_writer", "code_tester")
workflow.add_conditional_edges(
    "code_tester",
    should_continue,
    {
        "code_writer": "code_writer",
        "end": END
    }
)
app = workflow.compile()

# 運(yùn)行工作流
def main():
    initial_state = EvaluationState()
    result = app.invoke(initial_state)
    print("\n=== 評(píng)估結(jié)果 ===")
    print(f"最終狀態(tài)：{'通過' if result['passed'] else '失敗'}，經(jīng)過 {result['iteration']} 次迭代")
    print(f"最終代碼：\n{result['code']}")
    print(f"最終反饋：\n{result['feedback']}")
    print("\n迭代歷史：")
    for attempt in result["history"]:
        print(f"迭代 {attempt['iteration']}:")
        print(f"  代碼：\n{attempt['code']}")
        print(f"  反饋：{attempt['feedback']}")
        print(f"  是否通過：{attempt['passed']}\n")

if __name__ == "__main__":
    main()

from typing import Dict, Any, TypedDict, Literal
from langgraph.graph import StateGraph, END
from langchain_core.runnables import RunnableConfig
import re
import time

# 定義狀態(tài)
class TicketState(TypedDict):
    ticket_text: str
    category: str
    resolution: str
    processing_time: float

# 路由器智能體
def router_agent(state: TicketState) -> Dict[str, Any]:
    print("路由器智能體：分析工單...")
    start_time = time.time()
    ticket_text = state["ticket_text"].lower()
    if any(keyword in ticket_text for keyword in ["billing", "payment", "invoice", "charge"]):
        category = "Billing"
    elif any(keyword in ticket_text for keyword in ["technical", "bug", "error", "crash"]):
        category = "Technical"
    elif any(keyword in ticket_text for keyword in ["general", "question", "inquiry", "info"]):
        category = "General"
    else:
        category = "Unknown"
    processing_time = time.time() - start_time
    print(f"路由器智能體：分類為 '{category}'，耗時(shí) {processing_time:.2f} 秒")
    return {
        "category": category,
        "processing_time": processing_time
    }

# 賬單團(tuán)隊(duì)
def billing_team_agent(state: TicketState) -> Dict[str, Any]:
    print("賬單團(tuán)隊(duì)：處理工單...")
    start_time = time.time()
    ticket_text = state["ticket_text"]
    resolution = f"賬單團(tuán)隊(duì)：已審核工單 '{ticket_text}'。請(qǐng)檢查您的賬單詳情或聯(lián)系我們的賬單部門以獲取進(jìn)一步幫助。"
    processing_time = time.time() - start_time
    time.sleep(1)
    print(f"賬單團(tuán)隊(duì)：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "resolution": resolution,
        "processing_time": state["processing_time"] + processing_time
    }

# 技術(shù)支持團(tuán)隊(duì)
def technical_team_agent(state: TicketState) -> Dict[str, Any]:
    print("技術(shù)支持團(tuán)隊(duì)：處理工單...")
    start_time = time.time()
    ticket_text = state["ticket_text"]
    resolution = f"技術(shù)支持團(tuán)隊(duì)：已審核工單 '{ticket_text}'。請(qǐng)嘗試重啟您的設(shè)備或提交詳細(xì)的錯(cuò)誤日志以供進(jìn)一步調(diào)查。"
    processing_time = time.time() - start_time
    time.sleep(1.5)
    print(f"技術(shù)支持團(tuán)隊(duì)：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "resolution": resolution,
        "processing_time": state["processing_time"] + processing_time
    }

# 普通咨詢團(tuán)隊(duì)
def general_team_agent(state: TicketState) -> Dict[str, Any]:
    print("普通咨詢團(tuán)隊(duì)：處理工單...")
    start_time = time.time()
    ticket_text = state["ticket_text"]
    resolution = f"普通咨詢團(tuán)隊(duì)：已審核工單 '{ticket_text}'。更多信息，請(qǐng)參考我們的常見問題解答或通過電子郵件聯(lián)系我們。"
    processing_time = time.time() - start_time
    time.sleep(0.8)
    print(f"普通咨詢團(tuán)隊(duì)：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "resolution": resolution,
        "processing_time": state["processing_time"] + processing_time
    }

# 手動(dòng)審核團(tuán)隊(duì)
def manual_review_agent(state: TicketState) -> Dict[str, Any]:
    print("手動(dòng)審核團(tuán)隊(duì)：處理工單...")
    start_time = time.time()
    ticket_text = state["ticket_text"]
    resolution = f"手動(dòng)審核：工單 '{ticket_text}' 無法分類。標(biāo)記為人工審核。請(qǐng)手動(dòng)分配到適當(dāng)?shù)膱F(tuán)隊(duì)。"
    processing_time = time.time() - start_time
    time.sleep(0.5)
    print(f"手動(dòng)審核團(tuán)隊(duì)：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "resolution": resolution,
        "processing_time": state["processing_time"] + processing_time
    }

# 路由函數(shù)
def route_ticket(state: TicketState) -> Literal["billing_team", "technical_team", "general_team", "manual_review"]:
    category = state["category"]
    print(f"路由：工單分類為 '{category}'")
    if category == "Billing":
        return"billing_team"
    elif category == "Technical":
        return"technical_team"
    elif category == "General":
        return"general_team"
    else:
        return"manual_review"

# 構(gòu)建路由器模式的圖
def build_router_graph() -> StateGraph:
    workflow = StateGraph(TicketState)
    workflow.add_node("router", router_agent)
    workflow.add_node("billing_team", billing_team_agent)
    workflow.add_node("technical_team", technical_team_agent)
    workflow.add_node("general_team", general_team_agent)
    workflow.add_node("manual_review", manual_review_agent)
    workflow.set_entry_point("router")
    workflow.add_conditional_edges(
        "router",
        route_ticket,
        {
            "billing_team": "billing_team",
            "technical_team": "technical_team",
            "general_team": "general_team",
            "manual_review": "manual_review"
        }
    )
    workflow.add_edge("billing_team", END)
    workflow.add_edge("technical_team", END)
    workflow.add_edge("general_team", END)
    workflow.add_edge("manual_review", END)
    return workflow.compile()

# 運(yùn)行工作流
def main():
    test_tickets = [
        "I have a billing issue with my last invoice. It seems I was overcharged.",
        "My app keeps crashing with a technical error. Please help!",
        "I have a general question about your services. Can you provide more info?",
        "I need assistance with something unrelated to billing or technical issues."
    ]
    for ticket_text in test_tickets:
        initial_state: TicketState = {
            "ticket_text": ticket_text,
            "category": "",
            "resolution": "",
            "processing_time": 0.0
        }
        print(f"\n=== 處理工單：'{ticket_text}' ===")
        app = build_router_graph()
        start_time = time.time()
        result = app.invoke(initial_state, cnotallow=RunnableConfig())
        total_time = time.time() - start_time
        print("\n=== 工單結(jié)果 ===")
        print(f"分類：{result['category']}")
        print(f"解決方案：{result['resolution']}")
        print(f"總處理時(shí)間：{result['processing_time']:.2f} 秒")
        print(f"總耗時(shí)：{total_time:.2f} 秒")
        print("-" * 50)

if __name__ == "__main__":
    main()

from typing import Dict, Any, TypedDict, List
from langgraph.graph import StateGraph, END
from langchain_core.runnables import RunnableConfig
from textblob import TextBlob
import time
from typing_extensions import Annotated
from operator import add

# 定義狀態(tài)
class SocialMediaState(TypedDict):
    twitter_posts: List[str]
    instagram_posts: List[str]
    reddit_posts: List[str]
    twitter_sentiment: Dict[str, float]
    instagram_sentiment: Dict[str, float]
    reddit_sentiment: Dict[str, float]
    final_report: str
    processing_time: Annotated[float, add]

# 收集推特帖子
def collect_twitter_posts(state: SocialMediaState) -> Dict[str, Any]:
    print("推特智能體：收集帖子...")
    start_time = time.time()
    posts = [
        "Loving the new product from this brand! Amazing quality.",
        "Terrible customer service from this brand. Very disappointed."
    ]
    time.sleep(1)
    processing_time = time.time() - start_time
    print(f"推特智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "twitter_posts": posts,
        "processing_time": processing_time
    }

# 收集 Instagram 帖子
def collect_instagram_posts(state: SocialMediaState) -> Dict[str, Any]:
    print("Instagram 智能體：收集帖子...")
    start_time = time.time()
    posts = [
        "Beautiful design by this brand! #loveit",
        "Not impressed with the latest release. Expected better."
    ]
    time.sleep(1.2)
    processing_time = time.time() - start_time
    print(f"Instagram 智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "instagram_posts": posts,
        "processing_time": processing_time
    }

# 收集 Reddit 帖子
def collect_reddit_posts(state: SocialMediaState) -> Dict[str, Any]:
    print("Reddit 智能體：收集帖子...")
    start_time = time.time()
    posts = [
        "This brand is awesome! Great value for money.",
        "Had a bad experience with their support team. Not happy."
    ]
    time.sleep(0.8)
    processing_time = time.time() - start_time
    print(f"Reddit 智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "reddit_posts": posts,
        "processing_time": processing_time
    }

# 分析推特情感
def analyze_twitter_sentiment(state: SocialMediaState) -> Dict[str, Any]:
    print("推特情感智能體：分析情感...")
    start_time = time.time()
    posts = state["twitter_posts"]
    polarities = [TextBlob(post).sentiment.polarity for post in posts]
    avg_polarity = sum(polarities) / len(polarities) if polarities else0.0
    time.sleep(0.5)
    processing_time = time.time() - start_time
    print(f"推特情感智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "twitter_sentiment": {"average_polarity": avg_polarity, "num_posts": len(posts)},
        "processing_time": processing_time
    }

# 分析 Instagram 情感
def analyze_instagram_sentiment(state: SocialMediaState) -> Dict[str, Any]:
    print("Instagram 情感智能體：分析情感...")
    start_time = time.time()
    posts = state["instagram_posts"]
    polarities = [TextBlob(post).sentiment.polarity for post in posts]
    avg_polarity = sum(polarities) / len(polarities) if polarities else0.0
    time.sleep(0.6)
    processing_time = time.time() - start_time
    print(f"Instagram 情感智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "instagram_sentiment": {"average_polarity": avg_polarity, "num_posts": len(posts)},
        "processing_time": processing_time
    }

# 分析 Reddit 情感
def analyze_reddit_sentiment(state: SocialMediaState) -> Dict[str, Any]:
    print("Reddit 情感智能體：分析情感...")
    start_time = time.time()
    posts = state["reddit_posts"]
    polarities = [TextBlob(post).sentiment.polarity for post in posts]
    avg_polarity = sum(polarities) / len(polarities) if polarities else0.0
    time.sleep(0.4)
    processing_time = time.time() - start_time
    print(f"Reddit 情感智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "reddit_sentiment": {"average_polarity": avg_polarity, "num_posts": len(posts)},
        "processing_time": processing_time
    }

# 聚合結(jié)果
def aggregate_results(state: SocialMediaState) -> Dict[str, Any]:
    print("聚合智能體：生成最終報(bào)告...")
    start_time = time.time()
    twitter_sentiment = state["twitter_sentiment"]
    instagram_sentiment = state["instagram_sentiment"]
    reddit_sentiment = state["reddit_sentiment"]
    total_posts = (twitter_sentiment["num_posts"] +
                   instagram_sentiment["num_posts"] +
                   reddit_sentiment["num_posts"])
    weighted_polarity = (
        twitter_sentiment["average_polarity"] * twitter_sentiment["num_posts"] +
        instagram_sentiment["average_polarity"] * instagram_sentiment["num_posts"] +
        reddit_sentiment["average_polarity"] * reddit_sentiment["num_posts"]
    ) / total_posts if total_posts > 0else0.0
    overall_sentiment = ("Positive"if weighted_polarity > 0else
                         "Negative"if weighted_polarity < 0else"Neutral")
    report = (
        f"總體情感：{overall_sentiment} (平均極性：{weighted_polarity:.2f})\n"
        f"推特情感：{twitter_sentiment['average_polarity']:.2f} (帖子數(shù)：{twitter_sentiment['num_posts']})\n"
        f"Instagram 情感：{instagram_sentiment['average_polarity']:.2f} (帖子數(shù)：{instagram_sentiment['num_posts']})\n"
        f"Reddit 情感：{reddit_sentiment['average_polarity']:.2f} (帖子數(shù)：{reddit_sentiment['num_posts']})"
    )
    time.sleep(0.3)
    processing_time = time.time() - start_time
    print(f"聚合智能體：完成，耗時(shí) {processing_time:.2f} 秒")
    return {
        "final_report": report,
        "processing_time": processing_time
    }

# 構(gòu)建聚合模式的圖
def build_aggregator_graph() -> StateGraph:
    workflow = StateGraph(SocialMediaState)
    workflow.add_node("collect_twitter", collect_twitter_posts)
    workflow.add_node("collect_instagram", collect_instagram_posts)
    workflow.add_node("collect_reddit", collect_reddit_posts)
    workflow.add_node("analyze_twitter", analyze_twitter_sentiment)
    workflow.add_node("analyze_instagram", analyze_instagram_sentiment)
    workflow.add_node("analyze_reddit", analyze_reddit_sentiment)
    workflow.add_node("aggregate", aggregate_results)
    workflow.add_node("branch", lambda state: state)
    workflow.set_entry_point("branch")
    workflow.add_edge("branch", "collect_twitter")
    workflow.add_edge("branch", "collect_instagram")
    workflow.add_edge("branch", "collect_reddit")
    workflow.add_edge("collect_twitter", "analyze_twitter")
    workflow.add_edge("collect_instagram", "analyze_instagram")
    workflow.add_edge("collect_reddit", "analyze_reddit")
    workflow.add_edge("analyze_twitter", "aggregate")
    workflow.add_edge("analyze_instagram", "aggregate")
    workflow.add_edge("analyze_reddit", "aggregate")
    workflow.add_edge("aggregate", END)
    return workflow.compile()

# 運(yùn)行工作流
def main():
    initial_state: SocialMediaState = {
        "twitter_posts": [],
        "instagram_posts": [],
        "reddit_posts": [],
        "twitter_sentiment": {"average_polarity": 0.0, "num_posts": 0},
        "instagram_sentiment": {"average_polarity": 0.0, "num_posts": 0},
        "reddit_sentiment": {"average_polarity": 0.0, "num_posts": 0},
        "final_report": "",
        "processing_time": 0.0
    }
    print("\n開始社交媒體情感分析...")
    app = build_aggregator_graph()
    start_time = time.time()
    config = RunnableConfig(parallel=True)
    result = app.invoke(initial_state, cnotallow=config)
    total_time = time.time() - start_time
    print("\n=== 情感分析結(jié)果 ===")
    print(result["final_report"])
    print(f"\n總處理時(shí)間：{result['processing_time']:.2f} 秒")
    print(f"總耗時(shí)：{total_time:.2f} 秒")

if __name__ == "__main__":
    main()

from typing import Literal
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.types import Command

model = ChatOpenAI()

# 定義智能體 1
def agent_1(state: MessagesState) -> Command[Literal["agent_2", "agent_3", END]]:
    response = model.invoke(state["messages"])
    next_agent = response.get("next_agent", END)
    return Command(
        goto=next_agent,
        update={"messages": [response["content"]]}
    )

# 定義智能體 2
def agent_2(state: MessagesState) -> Command[Literal["agent_1", "agent_3", END]]:
    response = model.invoke(state["messages"])
    next_agent = response.get("next_agent", END)
    return Command(
        goto=next_agent,
        update={"messages": [response["content"]]}
    )

# 定義智能體 3
def agent_3(state: MessagesState) -> Command[Literal["agent_1", "agent_2", END]]:
    response = model.invoke(state["messages"])
    next_agent = response.get("next_agent", END)
    return Command(
        goto=next_agent,
        update={"messages": [response["content"]]}
    )

# 構(gòu)建圖
builder = StateGraph(MessagesState)
builder.add_node(agent_1)
builder.add_node(agent_2)
builder.add_node(agent_3)
builder.add_edge(START, "agent_1")
network = builder.compile()

from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.types import Command

# 定義一個(gè)智能體
def agent(state: MessagesState) -> Command[Literal["another_agent"]]:
    # 假設(shè) get_next_agent 是一個(gè)函數(shù)，用于決定下一個(gè)智能體
    next_agent = get_next_agent(state)
    return Command(
        goto=next_agent,
        update={"messages": ["Message from current agent"]}
    )

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.types import Command

model = ChatOpenAI()

# 定義監(jiān)督者
def supervisor(state: MessagesState) -> Command[Literal["agent_1", "agent_2", END]]:
    response = model.invoke(state["messages"])
    next_agent = response.get("next_agent", END)
    return Command(goto=next_agent)

# 定義智能體 1
def agent_1(state: MessagesState) -> Command[Literal["supervisor"]]:
    response = model.invoke(state["messages"])
    return Command(goto="supervisor", update={"messages": [response]})

# 定義智能體 2
def agent_2(state: MessagesState) -> Command[Literal["supervisor"]]:
    response = model.invoke(state["messages"])
    return Command(goto="supervisor", update={"messages": [response]})

# 構(gòu)建圖
builder = StateGraph(MessagesState)
builder.add_node(supervisor)
builder.add_node(agent_1)
builder.add_node(agent_2)
builder.add_edge(START, "supervisor")
supervisor_graph = builder.compile()

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.types import Command

model = ChatOpenAI()

# 定義團(tuán)隊(duì) 1 的監(jiān)督者
def team_1_supervisor(state: MessagesState) -> Command[Literal["team_1_agent_1", "team_1_agent_2", END]]:
    response = model.invoke(state["messages"])
    next_agent = response.get("next_agent", END)
    return Command(goto=next_agent)

# 定義團(tuán)隊(duì) 1 的智能體 1
def team_1_agent_1(state: MessagesState) -> Command[Literal["team_1_supervisor"]]:
    response = model.invoke(state["messages"])
    return Command(goto="team_1_supervisor", update={"messages": [response]})

# 定義團(tuán)隊(duì) 1 的智能體 2
def team_1_agent_2(state: MessagesState) -> Command[Literal["team_1_supervisor"]]:
    response = model.invoke(state["messages"])
    return Command(goto="team_1_supervisor", update={"messages": [response]})

# 構(gòu)建團(tuán)隊(duì) 1 的圖
team_1_builder = StateGraph(MessagesState)
team_1_builder.add_node(team_1_supervisor)
team_1_builder.add_node(team_1_agent_1)
team_1_builder.add_node(team_1_agent_2)
team_1_builder.add_edge(START, "team_1_supervisor")
team_1_graph = team_1_builder.compile()

# 定義頂層監(jiān)督者
def top_level_supervisor(state: MessagesState) -> Command[Literal["team_1_graph", END]]:
    response = model.invoke(state["messages"])
    next_team = response.get("next_team", END)
    return Command(goto=next_team)

# 構(gòu)建頂層圖
top_builder = StateGraph(MessagesState)
top_builder.add_node(top_level_supervisor)
top_builder.add_node("team_1_graph", team_1_graph)
top_builder.add_edge(START, "top_level_supervisor")
top_builder.add_edge("team_1_graph", "top_level_supervisor")
top_graph = top_builder.compile()

from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState, START

model = ChatOpenAI()

# 定義智能體 1
def agent_1(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": [response]}

# 定義智能體 2
def agent_2(state: MessagesState):
    response = model.invoke(state["messages"])
    return {"messages": [response]}

# 構(gòu)建圖
builder = StateGraph(MessagesState)
builder.add_node(agent_1)
builder.add_node(agent_2)
builder.add_edge(START, "agent_1")
builder.add_edge("agent_1", "agent_2")
custom_workflow = builder.compile()